Build Your Own Social Distancing Detection Tool Using Deep Learning and Computer Vision
Introduction
The COVID-19 pandemic has transformed the world and the way we live and interact with each other. One of the most effective ways to slow the spread of the virus is social distancing – maintaining a safe physical distance from others. While critical for public health, monitoring and enforcing social distancing can be challenging, especially in crowded public spaces.
In this tutorial, we‘ll harness the power of deep learning and computer vision to build an automated social distancing detection tool. By analyzing real-time video streams or images, our tool will detect people, measure the distances between them, and provide visual alerts when people get too close to each other.
This guide will walk you through the core concepts and code to build your own social distancing detector using state-of-the-art deep learning techniques. Whether you‘re an experienced practitioner or just getting started with computer vision, by the end of this tutorial you‘ll have a solid understanding of how to apply deep learning for object detection and build powerful video analytics applications. Let‘s get started!
A Primer on Object Detection
At the heart of our social distancing tool is object detection – the task of identifying and localizing objects of interest (in our case, people) within an image. Given an input image, an object detection model outputs a list of bounding boxes representing the location of each detected object, along with predicted class labels and confidence scores.
Object detection is a longstanding challenge in computer vision, with a wide range of approaches developed over the years. Traditional methods relied on hand-crafted features and classical machine learning algorithms. More recently, deep learning has emerged as the dominant paradigm, leveraging the power of convolutional neural networks (CNNs) to automatically learn rich, hierarchical feature representations from data.
Evolution of Object Detection Architectures
Modern object detectors are built upon a series of groundbreaking architectures:
R-CNN (Regions with CNN features): Proposed in 2014, R-CNN was one of the first deep learning-based object detectors to achieve state-of-the-art results. It works by:
- Generating region proposals using an external method like selective search
- Extracting CNN features for each region
- Classifying each region with SVMs
While accurate, R-CNN is very computationally expensive, as it runs the CNN forward pass for every single region proposal.
Fast R-CNN: An evolution of R-CNN that improves speed and accuracy by:
- Running the CNN just once on the full input image to get a feature map
- Using region of interest (RoI) pooling to extract a fixed-size feature vector for each region proposal from the shared feature map
- Feeding the RoI feature vectors into fully connected layers for classification and bounding box regression
Faster R-CNN: Eliminates the need for external region proposals by introducing a Region Proposal Network (RPN) that shares convolutional features with the object detection network, enabling nearly cost-free region proposals. The RPN and object detection network are trained end-to-end, resulting in further gains in speed and accuracy.
With each iteration, these detection architectures have grown more streamlined and performant. Faster R-CNN remains one of the most popular and widely used object detectors today, and will serve as the backbone for our social distancing tool.
Detectron2 – A PyTorch-based Modular Object Detection Library
Implementing state-of-the-art object detectors from scratch can be challenging, requiring careful design of the network architecture, loss functions, data processing pipelines, and training infrastructure. Fortunately, the fine folks at Facebook AI Research have open sourced Detectron2, a PyTorch-based library that makes it easy to build and train state-of-the-art object detection and segmentation models.
Out of the box, Detectron2 provides high-quality implementations of top object detection algorithms like Faster R-CNN and Mask R-CNN with a variety of different backbones. It also includes an extensible framework for training new models and a large model zoo of pretrained detection weights.
For our social distancing application, we‘ll use a Faster R-CNN model with a ResNet-50 FPN backbone, pretrained on the COCO dataset which covers 80 common object categories including people. Detectron2 makes this as simple as a few lines of code:
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
cfg = get_cfg()
cfg.merge_from_file("config file")
cfg.MODEL.WEIGHTS = "model.pkl"
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7
predictor = DefaultPredictor(cfg)
This loads the model configuration and pretrained weights, sets the classification confidence threshold to 0.7, and constructs a predictor object that we can use to run inference.
Building the Social Distancing Detector
Now that we have our object detection model ready to go, let‘s put the pieces together to build the full social distancing monitoring pipeline. We‘ll break this down step-by-step.
Step 1 – Detecting People in an Image
First, we‘ll write a function that takes an input image, runs the Detectron2 model to detect people, and returns a list of bounding box coordinates for each detected person.
def detect_people(image, predictor):
outputs = predictor(image)
boxes = []
for box, score, class_idx in zip(outputs["instances"].pred_boxes.tensor,
outputs["instances"].scores,
outputs["instances"].pred_classes):
if class_idx == 0: # person class
boxes.append(box.cpu().numpy().tolist())
return boxes
Step 2 – Compute Distances Between People
Next, we need a way to measure the physical distance between detected people. As the Detectron2 model gives us bounding boxes in pixel coordinates, we‘ll define a distance metric that makes some simplifying assumptions:
- We approximate each person as a point located at the center of the bottom edge of their bounding box. This corresponds to the location of their feet, which is a reasonable reference point for judging interpersonal distance.
- We compute Euclidean distance between these points as a proxy for real-world physical distance.
In reality, accurately estimating real-world distance would require more sophisticated techniques like camera calibration to account for perspective distortion. However, for a rough social distancing application, our simple approximation will suffice.
Here‘s a function to compute pairwise distances between detected people:
def compute_distances(boxes):
distances = []
points = []
for box in boxes:
x = (box[0]+box[2])/2
y = box[3]
points.append((x,y))
for i in range(len(points)):
for j in range(i+1, len(points)):
dist = sqrt((points[i][0]-points[j][0])2 + (points[i][1]-points[j][1])2)
distances.append((i,j,dist))
return distances
Step 3 – Visualize Results
Finally, let‘s visualize the results by drawing bounding boxes around detected people and connecting boxes that violate social distancing with red lines. We‘ll also add the measured distance in pixels as a label.
def visualize(image, boxes, distances, dist_thres=150):for i, box in enumerate(boxes): color = (0,255,0) for j,k,dist in distances: if i==j or i==k: if dist < dist_thres: color = (0,0,255) cv2.line(image, (int((box[0]+box[2])/2), box[3]), (int((boxes[j][0]+boxes[j][2])/2), boxes[j][3]), color, 2) cv2.putText(image, f"{int(dist)}", (int((box[0]+boxes[j][0])/2), int((box[1]+boxes[j][1])/2)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,0,0), 1)
cv2.rectangle(image, (int(box[0]),int(box[1])), (int(box[2]),int(box[3])), color, 2)cv2.imshow("Social Distance", image)
cv2.waitKey(1)
Putting it All Together
We can now combine these functions into a full social distancing monitoring pipeline:
def social_distance_detector(image): boxes = detect_people(image, predictor) distances = compute_distances(boxes) visualize(image, boxes, distances)To run this on a video stream, we simply need to iterate over the video frames and pass each frame to the
social_distance_detectorfunction:cap = cv2.VideoCapture(video_path) # video_path = 0 for webcamwhile True: _, image = cap.read() if image is None: break social_distance_detector(image)
And that‘s it! We now have a fully functioning social distancing monitoring tool. The detector will draw green boxes around people maintaining a safe distance and red boxes around those who are too close, with red connector lines showing the measured distance between them.
Future Directions
This tutorial provides a solid foundation for building a social distancing analysis tool, but there are many potential avenues for enhancement:
Camera Calibration: As mentioned earlier, estimating true physical distance from a 2D image requires accounting for the camera‘s perspective geometry. Techniques like homography can be used to map between image and ground plane coordinates if the camera parameters are known.
Multi-Camera Tracking: In many venues, a single camera may not cover the full area of interest. Integrating detections from multiple cameras and tracking people between views can provide more comprehensive monitoring coverage.
Crowd Counting: In addition to measuring distances between individuals, computer vision techniques can also estimate the total number of people in a scene, providing useful data for occupancy analysis and capacity planning.
Real-time Alerting: Integrating with real-time messaging or notification systems can enable proactive alerts to venue staff or public safety personnel when social distancing violations occur.
Privacy Considerations: As with any video analytics system, it‘s important to carefully consider privacy implications and implement appropriate safeguards like data minimization, secure transmission and storage, and clear usage policies. Face blurring or anonymization techniques can help preserve individual privacy.
Conclusion
In this post, we‘ve seen how state-of-the-art deep learning models can be leveraged to build powerful video analysis tools for social distancing monitoring. Starting with a pretrained Faster R-CNN model, we defined methods for detecting people, measuring distances between them, and visualizing the results in an intuitive way.
While we‘ve focused on social distancing as a motivating application, the techniques covered here are highly versatile and can be adapted to a wide range of object detection and tracking scenarios. The key ingredients – a robust detection model, a library of image processing and visualization utilities, and a bit of problem-solving creativity – are a recipe for endless computer vision applications.
I hope this guide has given you a taste of the incredible potential of deep learning for video analytics and a starting point for your own projects. So go forth and build amazing things! And of course, stay safe and socially distanced.
